“Machine learning to detect drunk driving” accepted at CHI 2023

The ACM Conference on Human Factors in Computing Systems (CHI 2023) accepted our paper “Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving”. The ACM CHI is the premier international conference of Human-Computer Interaction (HCI) with an h5-index of 113 and an h5-median of 154. The preprint of our publication is available on arXiv.

Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical mo- ments such as driving. This requires real-time information on a person’s blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in nu- merous countries worldwide. We evaluate our system with = 30 participants in an interventional simulator study. Our system reli- ably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological ef- fects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the po- tential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm.
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Kevin Koch
Kevin KochResearcher
Robin Deuber
Robin DeuberResearcher